Journal of Advanced Transportation最新文献

{"title":"A Route Diversity–Based Approach for Estimating Vulnerability of Stations in a Multimodal Public Transport Network","authors":"Jianlin Jia,&nbsp;Yuwen Huang,&nbsp;Wanting Zhang,&nbsp;Yanyan Chen,&nbsp;Zhuo Liu","doi":"10.1155/2024/6995651","DOIUrl":"https://doi.org/10.1155/2024/6995651","url":null,"abstract":"<div>\u0000 <p>Multimodal public transport network (MPTN) plays an important role in relieving road traffic pressure for metropolitan area. Nevertheless, the impact of an accident happened in an individual station may not only disrupt the station itself or the single lines that go through the station but also spread over the whole network. Therefore, identifying the vulnerable stations is essential for improving the MPTN management against the systematic risk caused by accidents. In this paper, we proposed a route diversity-based approach to measure the vulnerability of stations in MPTN based on the complex network theory. The route constraint parameters were established to reflect the travel time restriction in constructing the set of passengers’ acceptable routes. In addition, an algorithm was formulated to rapidly calculate the route diversity index and meanwhile avoid the “overlapping routes” problem. A simple virtual network was used as a numerical example to compare the proposed approach with the vulnerability evaluation approaches based on degree centrality and betweenness centrality. Finally, the proposed approach was applied to the MPTN of Beijing to explain its effectiveness and potential applications. The results show that the proposed method can efficaciously estimate vulnerable nodes compared with degree centrality and betweenness centrality. Meanwhile, the acceptable routes between any OD pairs in the MPTN are 1–10 according to the constrained parameter. In addition, the average number of acceptable routes between OD pairs of Beijing MPTN is 3.649. By ranking the stations according to their vulnerability, it can be found that the top 5 vulnerable stations are all external traffic hubs or the stations around famous commercial areas. The results suggest that these stations are significant for external transport as well as crucial for internal urban transportation systems. The research output could contribute to the MPTN management in accident prevention and emergency handling.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/6995651","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142641445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the Urban Transport System Resilience Through Adaptive Traffic Signal Control Enabled by Decentralised Multiagent Reinforcement Learning","authors":"Xiangmin Yang,&nbsp;Yi Yu,&nbsp;Yuxiang Feng,&nbsp;Washington Yotto Ochieng","doi":"10.1155/2024/3035753","DOIUrl":"https://doi.org/10.1155/2024/3035753","url":null,"abstract":"<div>\u0000 <p>The principle of system resilience is its ability to withstand disruptions and maintain an equilibrium state. In urban network systems, adaptive traffic signal control (ATSC) has been an effective countermeasure to mitigate traffic flow disturbance and improve resilience. This research has explored the usage of a decentralised advantage actor-critic (a2c) algorithm-based ATSC in mitigating disruptions, particularly nonrecurring congestion caused by car accidents. A reward function has also been proposed, combining deduced resilience metric, safety indicator time to collision (TTC) and system performance. A virtual simulation environment was created using simulation of urban mobility (SUMO) to facilitate the evaluation of the proposed approach. In the grid simulation environment, an overall 5.8% improvement is achieved, exceeding benchmark algorithms in three metrics, especially performance with a margin of over 5.2%. Robustness against different levels of car accidents are proven as well. Further evaluation is also implemented based on a real-world case study and demonstrates an improvement of 20.08%, highlighting the correlation of proposed method’s efficiency on the traffic flow rate and road structure.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/3035753","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142641550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification and Prediction of Vehicle Lane-Changing Crash Risk Levels Based on Video Trajectory Data","authors":"Shijie Gao,&nbsp;Lanxin Jiao,&nbsp;Haiyue Wang,&nbsp;Xiu Pan,&nbsp;Yixian Li,&nbsp;Jiandong Zhao","doi":"10.1155/2024/9437594","DOIUrl":"https://doi.org/10.1155/2024/9437594","url":null,"abstract":"<div>\u0000 <p>To investigate the potential lane-changing collision risks that may arise between vehicles during lane changes and those in the original lane, a model for vehicle lane-changing collision risk is constructed specifically for this scenario, and a research analysis is conducted. First, based on vehicle trajectory data, a sample set capturing the relationships between vehicles traveling in a straight line and those changing lanes laterally is extracted and built. Interpolation methods are then applied to fill in missing values, outliers are eliminated, and data noise is smoothed during preprocessing. After preprocessing, a total of 468 vehicle pairs and 265,392 data points are obtained. Second, a real-time collision time model is established based on the preprocessed data, and collision risk probabilities are calculated accordingly. Then, the collision risks are classified into four levels based on whether the vehicle on the side actually changes lanes and the severity of the collision risks. Finally, a light gradient boosting machine (LightGBM) learning method is adopted to predict the risk levels and analyze the main factors that significantly impact the severity of collision risks. The results indicate that the longitudinal distance between the target vehicle and the preceding vehicle is the most critical influencing factor, followed by the speed of the target vehicle itself, and then the speed difference between the target vehicle and the preceding vehicle. The influence of other factors is relatively similar and does not have a significant impact.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/9437594","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142641262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of Modified Temporal Safety Performance Function Considering Various Time Flows","authors":"Yeji Sung,&nbsp;Seunghwan Kim,&nbsp;Juneyoung Park,&nbsp;Ling Wang","doi":"10.1155/2024/7970454","DOIUrl":"https://doi.org/10.1155/2024/7970454","url":null,"abstract":"<div>\u0000 <p>Safety performance functions (SPFs) have become valuable tools for estimating the relationships between crashes and various causal factors when constructing crash-prediction models. However, the commonly used independent variable, the annual average daily traffic (AADT) is data on a yearly basis, which has limitations in capturing the temporal characteristics of traffic flows influenced by the passage of time. Accordingly, there have also been many studies using 15 min data to reflect real-time, which is an important time unit to understand changes in highway traffic flow. However, such a short time unit has the limitation of high instability and randomness. In light of this, this study recognizes the importance of the 15 min time interval and proposes a new approach by developing a modified hourly model that aggregates data at fine-grained 15 min intervals (00, 15, 30, and 45 min, both at the beginning and end), instead of the traditional hourly data that starts and ends at the peak of each hour to compensate for the existing limitations. The analysis focused on South Korea’s nationwide highways, and models were developed based on both statistical and machine-learning approaches to compare their performances for selecting the final model. Additionally, a modified temporal SPF is introduced to predict crashes by assigning weights based on a Dirichlet distribution to models with overlapping time intervals aggregated in 15 min increments. This innovative approach overcomes the limitations of existing 15 min models, where the number of crashes is too small for effective training if the model is simply developed by dividing the time. The anticipated outcome is that the proposed model will demonstrate excellent performance and serve as an effective tool for predicting highway crash risks.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7970454","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142588175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Longitudinal Hierarchical Control of Autonomous Vehicle Based on Deep Reinforcement Learning and PID Algorithm","authors":"Jialu Ma,&nbsp;Pingping Zhang,&nbsp;Yixian Li,&nbsp;Yuhang Gao,&nbsp;Jiandong Zhao","doi":"10.1155/2024/2179275","DOIUrl":"https://doi.org/10.1155/2024/2179275","url":null,"abstract":"<div>\u0000 <p>Longitudinal control of autonomous vehicles (AVs) has long been a prominent subject and challenge. A hierarchical longitudinal control system that integrates deep deterministic policy gradient (DDPG) and proportional–integral–derivative (PID) control algorithms was proposed in this paper to ensure safe and efficient vehicle operation. First, a hierarchical control structure was employed to devise the longitudinal control algorithm, utilizing a Carsim-based model of the vehicle’s longitudinal dynamics. Subsequently, an upper controller algorithm was developed, combining DDPG and PID, wherein perceptual information such as leading vehicle speed and distance served as input state for the DDPG algorithm to determine PID parameters and output the desired acceleration of the vehicle. Following this, a lower controller was designed employing a PID-based driving and braking switching strategy. The disparity between the desired and actual accelerations was fed into the PID, which calculated the control acceleration to enact the driving and braking switching strategy. Finally, the effectiveness of the designed control algorithm was validated through simulation scenarios using Carsim and Simulink. Results demonstrate that the longitudinal control method proposed herein adeptly manages vehicle speed and following distance, thus satisfying the safety requirements of AVs.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/2179275","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142574060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Factors Influencing Public Acceptance of Air Taxis in South Korea","authors":"Ansun Park,&nbsp;Seungmin Lee","doi":"10.1155/2024/6555597","DOIUrl":"https://doi.org/10.1155/2024/6555597","url":null,"abstract":"<div>\u0000 <p>Air taxis, a core service within urban air mobility (UAM), have the potential to enhance user satisfaction and address societal challenges such as traffic congestion and environmental pollution. However, the success of this service is often hindered by various concerns. To ensure successful implementation, we investigate the factors influencing public acceptance of air taxis. This study distinguishes itself from previous research in three key aspects. First, it introduces a novel classification of the factors into individual and societal dimensions. Second, it is among the first to apply a value-based adoption model to understand the intention to adopt air taxis, including UAM. Third, it uniquely considers the Korean perspective, unlike most existing studies that focus on Western cultural contexts. To identify the consumers’ perceptions, we conducted interviews with experts and surveyed a sample of 1,000 members of the general public in Korea. Our findings suggest that perceived value for society, as well as perceived value for individual users, significantly influences adoption intention. We discuss both academic insights and practical implications for policy and industry, supporting the commercialization of Korean UAM (K-UAM) promoted by the Korean government.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/6555597","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decision-Making and Path Planning for Head-On Collision Avoidance on Curved Roads","authors":"Masoud Abdollahinia,&nbsp;Ali Ghaffari,&nbsp;Shahram Azadi","doi":"10.1155/2024/8171722","DOIUrl":"https://doi.org/10.1155/2024/8171722","url":null,"abstract":"<div>\u0000 <p>Deviating to the left on two-way roads can result in fatal head-on collisions. This article presents an intelligent decision-making and path-planning algorithm aimed at avoiding collision with a vehicle that has deviated from the opposing lane. The path-planning process utilizes the model predictive control (MPC) approach, employing a linear kinematic prediction model with a horizon of 2 seconds. Considering that the deviated vehicle may abruptly return to its original lane at any moment, its motion is associated with significant uncertainty. To address this challenge, the path-planning algorithm directs the ego vehicle (EV) under specific constraints to ensure that both the left and right sides of the road are symmetrically reachable in future time steps. This enables the decision-making algorithm to select the safer direction for evasive maneuver at the appropriate moment. The motion prediction of the threat vehicle (TV) is conducted until the potential collision time, taking into account its motion history, and is utilized in the decision-making process. Once the maneuver direction is determined, the collision-free path planning continues using the MPC method. To evaluate the algorithm, six simulations are conducted, modeling various distant and close encounter states of the vehicles on roads with left- and right-hand curves. The simulation results indicate the flexibility and appropriate performance of the algorithm in planning safe and maneuverable paths.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/8171722","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The “Visual-Behavior” Chain and Risk Prediction Model for Sedan Drivers Under the Influence of Container Trucks: A Case Study of Yangshan Port Freight Corridor","authors":"Yi Li,&nbsp;Zhitian Wang,&nbsp;Fengchun Yang,&nbsp;Minghui Li","doi":"10.1155/2024/5564381","DOIUrl":"https://doi.org/10.1155/2024/5564381","url":null,"abstract":"<div>\u0000 <p>With the development of the Shanghai International Shipping Center, the diversity of vehicle types on the highways and arterial roads near Yangshan port is continually increasing. Within such a container port corridor, large container trucks are primarily utilized for mainline transportation. Their larger size and significant inertia would increase psychological pressure on sedan drivers, and elevate their behavior risk. To investigate the effects of container trucks on drivers’ visual characteristics and driving behavior as well as to predict driving risk, firstly, this research conducted field tests in four scenarios surrounding the port. Visual characteristics and behavior data of sedan drivers were collected. Secondly, a “Visual-behavior” chain model was established. The relationship between drivers’ visual characteristics, driving behavior, and driving risk was illustrated from the perspective of time-series behavior patterns. Thirdly, three driving risk prediction models were built with Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and ARIMA-LSTM. The results indicate that the ARIMA-LSTM model shows the most effective prediction performance. This research provides a field-data comparative analysis of the driving risks influenced by a high proportion of container trucks. The findings contribute to understanding the unique mixed traffic visual environment around large-scale container ports.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5564381","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstruction of the Motion of Traffic Accident Vehicle in the Vehicle-Mounted Video Based on Direct Linear Transform","authors":"Hao Feng,&nbsp;Feng Chen,&nbsp;Weiwei Heng","doi":"10.1155/2024/5793435","DOIUrl":"https://doi.org/10.1155/2024/5793435","url":null,"abstract":"<div>\u0000 <p>Based on the principle of direct linear transformation (DLT) in close-range photogrammetry, a method was proposed for reconstructing the motion states of the host vehicle and other vehicles based on vehicle-mounted videos. To verify the effectiveness and accuracy of the method, validation experiments were designed. Under two typical operating states, steering and straight driving, the motion states of the host vehicle and other vehicles (including trajectory, distance, speed, and acceleration) were reconstructed from the vehicle-mounted video. In the experiments, high-precision inertial navigation was installed on the other vehicle to record real-time motion data of the vehicle. Finally, in order to compare and analyze the reconstructed video results with the vehicle’s actual motion data, the recorded motion data were matched and synchronized to the same time axis as the vehicle-mounted videos through a GPS timing device. The experimental result shows that the reconstructed trajectory results based on this method can generally reflect the vehicle’s actual trajectory, with an average deviation of less than 7.4%; the reconstructed distance results have an average deviation of less than 9.3%; the reconstructed speed results have an average deviation of less than 7.3%; the reconstructed acceleration results can reflect the vehicle’s acceleration or deceleration states. The results of this study provide an effective solution for obtaining important parameters of vehicles in accident reconstruction research, such as the trajectory, speed, distance, and acceleration or deceleration, and it has significant practical value for applications.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5793435","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vehicle Lane Change Multistep Trajectory Prediction Based on Data and CNN_BiLSTM Model","authors":"Shijie Gao,&nbsp;Zhimin Zhao,&nbsp;Xinjian Liu,&nbsp;Yanli Jiao,&nbsp;Chunyang Song,&nbsp;Jiandong Zhao","doi":"10.1155/2024/7129562","DOIUrl":"https://doi.org/10.1155/2024/7129562","url":null,"abstract":"<div>\u0000 <p>In order to accurately predict the lane-changing trajectory of the vehicle and improve the driving safety of the vehicle, a lane-changing trajectory prediction model based on the combination of convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) neural network is proposed by comprehensively considering the historical driving behavior, the spatial characteristics of surrounding vehicles and the bidirectional time sequence information of the vehicle trajectory. Firstly, the vehicle trajectory data are filtered and smoothed, and it is divided into three categories: left lane change, right lane change, and straight driving, and a lane change trajectory sample set is constructed. Secondly, CNN-BiLSTM model is constructed to identify the sample set of lane-changing trajectory. Considering the interaction between vehicles in the driving process, the information of predicted vehicle, and surrounding vehicles is taken as the input of the model. The extracted feature vector is input to the BiLSTM layer for prediction after the CNN layer feature extraction, and the horizontal and vertical coordinates of the target vehicle at the next time are output. Thirdly, the trajectory data of the US-101 dataset in NGSIM is selected to verify the performance of the CNN-BiLSTM model, and at the same time, it is compared with models such as CNN-LSTM, long short-term memory (LSTM), BiLSTM, and CNN-GRU-Att. Finally, the verification result shows that the overall fitting degree of the vehicle lane change trajectory prediction of the proposed model reaches 99.50%, and the mean square error and mean absolute error are 0.0003076 and 0.01417, which are improved compared with other models. In the meanwhile, the research on multistep trajectory prediction in different prediction time domains is carried out. It was found that the longer the prediction time domain is, the lower the prediction performance of the model decreases, but the prediction accuracy still reached more than 96%, and it was able to accurately predict the lane change trajectory.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7129562","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}